AIRLINES OPERATIONS REPORT¶

Using airlines data from https://www.kaggle.com/datasets/ayushparwal2026/airlines-dataset ¶

Goal :¶

To analyze airlines data, providing insights into the landing counts and total landed weight for various airlines and aircraft types.
Examine how landing data varies across different GEO regions.
Identify patterns and trends in landing data based on different aircraft types and manufacturers.
Create visualizations that stakeholders can use to make informed decisions regarding airline operations

Overview : This dataset contains below fields¶

Operating Airline Operating Airline IATA Code Published Airline Published Airline IATA Code GEO Summary GEO Region Landing Aircraft Type Aircraft Body Type Aircraft Manufacturer Aircraft Model Landing Count Total Landed Weight

In [8]:

# Step 1: Load the Dataset
import pandas as pd

url = "https://shirleymsassignments.github.io/altair/cleaned_air.csv"

data= pd.read_csv(url)
data.head()

Out[8]:

	Operating Airline	Operating Airline IATA Code	Published Airline	Published Airline IATA Code	GEO Summary	GEO Region	Landing Aircraft Type	Aircraft Body Type	Aircraft Manufacturer	Aircraft Model	Landing Count	Total Landed Weight
0	SkyWest Airlines	OO	United Airlines	UA	International	Canada	Passenger	Regional Jet	Bombardier	CRJ2	30	1410000
1	Air Canada	AC	Air Canada	AC	International	Canada	Passenger	Narrow Body	Airbus	A320	125	17787978
2	Japan Airlines	JL	Japan Airlines	JL	International	Asia	Passenger	Wide Body	Boeing	B773	30	16620000
3	COPA Airlines, Inc.	CM	COPA Airlines, Inc.	CM	International	Central America	Passenger	Narrow Body	Boeing	B739	3	491700
4	Hong Kong Airlines Limited	HX	Hong Kong Airlines Limited	HX	International	Asia	Passenger	Wide Body	Airbus	A359	16	7301712

Used below altair charts for visualizations¶

Bar chart used to compare landing counts across airlines
scatter plot (Total Landed Weight vs. Landing Count) to visualize correlations and patterns with aircraft types
Filtered Bar Chart for Specific GEO Region - 'Canada'
Interactive Selection to interactively explore data for specific airlines
Scatterplot Matrix (SPLOM):providing insights across multiple variables

Visualization 1: Bar chart¶

Provides a clear visual representation of which airlines have the highest and lowest landing counts, crucial for operational insights and strategic planning¶

In [9]:

import altair as alt

# Disable the max rows limit
alt.data_transformers.disable_max_rows()
alt.Chart(data).mark_bar().encode(x="GEO Region", y="Landing Count")

Out[9]:

Visualization 2: Interactive Line chart of Landing Count by Operating Airline¶

Interactive features such as selection and tooltips enhance user interaction allowing users to explore data dynamically and gain deeper insights.¶

In [10]:

alt.Chart(data).mark_line().encode(
    y='Landing Count',
    x='Operating Airline',
    tooltip=['Operating Airline', 'Landing Count']
).properties(
    title='Landing Count by Operating Airline'
).interactive()

Out[10]:

In [ ]:

Visualization 3 : Altair circle chart¶

Focuses on regional insights, allowing users to identify regional trends and tailor strategies accordingly. In this visualization , used region vs. Landing count¶

In [11]:

alt.Chart(data).mark_circle().encode(
    x ="GEO Region", y="Landing Count",
    color=alt.Color('GEO Region', scale=alt.Scale(scheme='spectral'))
)

Out[11]:

Visualization 4: Filtered bar chart¶

Landing Count by Aircraft Manufacturer for a specific GEO Region¶

In [12]:

geo_region = 'Canada'
alt.Chart(data[data['GEO Region'] == geo_region]).mark_bar().encode(
    x='Landing Count',
    y='Aircraft Manufacturer',
    tooltip=['Aircraft Manufacturer', 'Landing Count']
).properties(
    title=f'Landing Count by Aircraft Manufacturer in {geo_region}'
).interactive()

Out[12]:

Filtered Bar Chart: Landing Count by Aircraft Manufacturer in US¶

This chart shows the landing count distribution for aircraft manufacturers in the US region. Hover over the bars to see detailed information about each manufacturer's landing count.

Interactive Scatter Plot: Landing Count vs. Total Landed Weight¶

This scatter plot allows you to interactively explore the relationship between landing count and total landed weight across different operating airlines. Use the legend to select specific airlines for comparison.

In [13]:

geo_region = 'US'
filtered_chart = alt.Chart(data[data['GEO Region'] == geo_region]).mark_bar().encode(
    x='Landing Count:Q',
    y=alt.Y('Aircraft Manufacturer:N', sort='-x'),
    tooltip=['Aircraft Manufacturer', 'Landing Count']
).properties(
    title=f'Landing Count by Aircraft Manufacturer in {geo_region}'
).interactive()

# Visualization 4: Interactive selection for Operating Airline
selection = alt.selection_point(fields=['Operating Airline'], bind='legend')
interactive_chart = alt.Chart(data).mark_point().encode(
    x='Landing Count:Q',
    y='Total Landed Weight:Q',
    color=alt.condition(selection, 'Operating Airline:N', alt.value('lightgray')),
    tooltip=['Operating Airline', 'Landing Count', 'Total Landed Weight']
).add_params(
    selection
).properties(
    title='Interactive Selection for Operating Airline'
)

# Display the charts
filtered_chart | interactive_chart

Out[13]:

Visualization 6: Build a SPLOM¶

Comprehensive overview of data relationships, allowing users to identify correlations and patterns across various variables simultaneously, which is crucial for comprehensive analysis and planning¶

In [14]:

splom = alt.Chart(data).mark_circle().encode(
    alt.X(alt.repeat("column"), type="nominal", title=None),
    alt.Y(alt.repeat("row"), type="nominal", title=None),
    color=alt.Color('Landing Count:Q', scale=alt.Scale(type='log'), title='Landing Count'),
    tooltip=["Operating Airline", "Landing Count", "GEO Region"]
).properties(
    width=125,
    height=125
).repeat(
    row=["GEO Region", "Landing Aircraft Type", "Aircraft Manufacturer"],
    column=["GEO Region", "Landing Aircraft Type", "Aircraft Manufacturer"]
).configure_axis(
    domain=True,
    ticks=True,
    grid=True,
    labels=True
).configure_view(
    strokeWidth=0
)

# Display the SPLOM
splom

Out[14]:

Evaluations :¶

person1 : asked to compare landing counts among different operating airlines to identify which airlines had the highest and lowest activity.

Feedback : Found the bar chart of landing counts by operating airline informative. Suggested adding a feature to filter airlines by body type.

person2 : asked to analyze landing data for a specific GEO region to understand regional variations in aircraft manufacturer preferences.

Feedback : used the filtered bar chart to explore landing counts by aircraft manufacturer in the chosen region. Recommended clearer labeling of manufacturer names

Synthesis of Findings:¶

Evaluation around specific tasks allowed users to interact with the visualizations in contextually relevant scenarios. This has helped providing meaningful feedback on usability and effectiveness in real-world applications.

Incorporating interactive elements such as selection tools, tooltips, and filtering options enhanced user engagement

The use of multiple visualization types (e.g., bar charts, scatter plots, filtered views) provided a holistic view of landing data, enabling stakeholders to analyze trends across different variables like operating airlines, geographic regions, and aircraft characteristics

Need to implement advanced interactive capabilities such as dynamic dashboards, or predictive analytics to deepen the analytical capabilities of the visualizations. Standardize design for clarity and coherence.

Optimizing performance for larger datasets or more complex visualizations is essential

Clear instructions on how to use interactive tools and interpret visual elements to enhance usability